Disease Progression In Models Research Articles

Leveraging Large‐scale CSF Proteomics to identify Disease Progression Biomarkers in Autosomal Dominant Frontotemporal Lobar Degeneration

AbstractBackgroundThe pathophysiological mechanisms driving frontotemporal lobar degeneration (FTLD) disease progression and corresponding biomarkers that capture these changes are not fully understood. We leveraged high‐throughput proteomics to identify networks of co‐expressed CSF proteins and candidate biomarkers for prediction of disease progression in adults with autosomal dominant FTLD mutations.Method113 adults carrying autosomal dominant FTLD mutations (C9orf72/GRN/MAPT) and 38 noncarrier controls from the ALLFTD consortium completed baseline lumbar puncture and longitudinal neuropsychological testing. CSF was assayed for SOMAmer proteomics on SOMAscan® v3.0 (4,140 proteins). Network analysis identified weighted protein co‐expression modules, which were annotated via gene ontology and cell‐type enrichment analysis. Disease age estimates that capture proximity to symptom onset were derived from a Bayesian disease progression model incorporating genotype‐specific clinical (cognition, CDR®+NACC FTLD) and biomarker (brain volume, plasma NfL) information. At baseline, local‐weighted regression examined module z‐scores (based on mean/SD of module levels [first principal component] in noncarriers) in relation to disease age. Longitudinal linear mixed‐effects models examined global cognitive trajectories in relation to baseline module z‐scores, as well as individual proteins within prognostic modules.ResultNetwork analysis identified 32 CSF protein co‐expression modules (37 to 223 proteins/module). 16 modules showed mutation‐related deviations from noncarriers. Deviations in ion transport, neurodevelopmental, metabolic, and transcriptional modules occurred several decades before estimated symptom onset (disease age: ‐30 to ‐20 years) and persisted across disease age. Deviations in neuroinflammatory and ubiquitination/autophagy modules occurred ∼10 years before estimated symptom onset. Sharp deviations in RNA binding, protein phosphorylation, neurogenesis, synaptic function, and lysosomal modules occurred near symptom onset (disease age: ‐1 to 4 years). Baseline synaptic and neurogenesis modules were the strongest prognosticators of cognitive decline (module x time: bs>.10, ps<.05). Within these modules, individual proteins with stronger intramodular connectivity exhibited stronger associations with cognitive decline (e.g., synaptic/transmembrane module: r = .40, p = 2.85e‐6; e.g., EPHA5, LRRTM2).ConclusionLarge‐scale analysis of the CSF proteome reveals a biologically‐diverse landscape of protein networks associated with presymptomatic and symptomatic stages of autosomal dominant FTLD. Synaptic and neuron‐enriched protein networks exhibited prodromal deviations that forecasted incipient cognitive decline. ‘Hub’ proteins derived from these affected networks warrant further validation as candidate biomarkers for early FTLD disease progression.

Predicting Disease Progression from Idiopathic Cytopenia of Undetermined Significance to Myeloid Malignancies Using a Machine Learning-Based Approach

Background Idiopathic cytopenia of undetermined significance (ICUS) is characterized by an unexplained, persistent cytopenia without a definite diagnosis of hematologic or non-hematologic conditions, and that does not meet criteria for myeloid neoplasms. The progression of ICUS to myeloid malignancies has been associated with certain clinical features, such as genetic mutations or allele frequencies. However, the incidence of progression and the risk factors vary among previous studies. While prognostic scoring systems are well-established in myelodysplastic syndrome (MDS) to predict progression to acute myeloid leukemia and survival outcomes, similar predictive tools for ICUS are currently lacking. Considering that a significant proportion of patients with ICUS share similar genetic abnormalities and clinical features with those with MDS, there is a need for predictive tools for ICUS. In this study, we aimed to establish a predictive model for disease progression from ICUS to myeloid malignancies. Methods Patients with ICUS were identified using electronic medical records, and those meeting the diagnostic criteria of ICUS were included. ICUS was defined as persistent cytopenia lasting for at least 4 months, with no underlying disease or condition that could explain the cytopenia which included history of cytotoxic chemotherapy or radiation therapy, blood or bone marrow (BM) disorders, autoimmune diseases, solid organ transplantation, active infection by atypical pathogens, and the use of immunosuppressant and chemotherapeutic agents. Cytopenia was defined as follows: hemoglobin <13 g/dL in males or <12 g/dL in females, neutrophil count <1.9 × 10 9/L, and/or platelets <150 × 10 9/L. For the predictive model, various features were considered, including medication history, laboratory data, comorbidities, family history of cancer and major disorders, smoking history, alcohol consumption, and physical measurements. Numeric data from the BM report were standardized using regular expressions, while free text or comments from the BM report and chromosomal analysis were extracted and processed using a pre-trained language model called PubMedBERT to generate embeddings. The primary objective of this study was to develop a predictive model for identifying progression from ICUS to myeloid malignancies. The performance of the model was assessed using machine learning algorithms, specifically XGBoost, support vector machine, and logistic regression. Results Among the initial 6,962 patients with medical records of the BM examination between January 2000 and December 2021, 5,147 patients were excluded from the modeling process due to secondary causes of cytopenia. Ultimately, 1,815 patients were finally included in the study. Of 1,815 patients, 47 (2.6%) were diagnosed with myeloid malignancies from further BM examination. The predictive model, utilizing XGBoost and incorporating standardized text and embedded features, demonstrated favorable performance, with a mean area under the receiver operating characteristic curve of 0.800 (Figure). Additionally, our analysis confirmed that the XGBoost model effectively processed features from text data, as we assessed the feature importance using Shapley values. Conclusion In conclusion, our study successfully established a predictive model for disease progression from ICUS to myeloid malignancies, demonstrating the potential utility of clinical predictive tools. Further integration of genetic data holds promise for enhancing the model's performance and providing clinical insights for managing patients with ICUS.

Risk prediction of the progression of chronic kidney disease stage 1 based on peripheral blood samples: construction and internal validation of a nomogram

Patients with chronic kidney disease (CKD) have high morbidity and mortality, and the disease progression has a significant impact on their survival and living standards. This research aims to analyze risk factors for CKD stage 1 and provide a reference for clinical decision making. The clinical data and peripheral blood samples of 300 patients with CKD stage 1 were collected retrospectively. Patients were randomly assigned into a training set (n = 210) and a validation set (n = 90). Patients’ baseline characteristic levels were subjected to statistical tests for difference. Univariate and multivariate Cox regression analyses were utilized to identify risk factors influencing disease progression. Subsequently, a prediction model for disease progression was developed using a nomogram, and the model’s accuracy was assessed using the C-index and calibration curve. The results revealed that hypertension, diabetes, and urinary albumin were essential factors in the progression of CKD stage 1. The nomogram was constructed and then the C-index was calculated. The calibration curve was utilized to assess the risk model. The C-index of the training set was 0.75, and the C-index of the validation set was 0.73, suggesting a good predictive ability of the model. The risk model accurately predicted the progression of CKD stage 1, which is of great significance to developing personalized treatment for patients in clinical practice.

Cost-effectiveness analysis of encorafenib and binimetinib combination as first-line treatment for metastatic or unresectable <i>BRAF</i> V600-mutated metastatic melanoma in Russia

Objective: to perform a cost-effectiveness analysis of using encorafenib and binimetinib combination in the first-line therapy for metastatic or unresectable BRAF V600-mutated melanoma in the Russian Federation. Material and methods. The comparators included “dabrafenib + trametinib” combination and vemurafenib monotherapy, which are listed in the Russian clinical guidelines and the Vital and Essential Drugs List. Since “encorafenib and binimetinib” combination demonstrated superior overall response rate compared to “dabrafenib + trametinib” and vemurafenib monotherapy, the method of cost-effectiveness analysis was used. We developed a disease progression model based on data from the COLUMBUS randomized control trial and published network meta-analysis. The model was used to calculate the direct medical costs associated with the considered alternatives over a 3-year modeling horizon. We compared the incremental cost-effectiveness ratio for “encorafenib + binimetinib” vs. “dabrafenib + trametinib” to the same ratio calculated for “dabrafenib + trametinib” vs. vemurafenib monotherapy.Results. Mean costs of using “encorafenib + binimetinib” combination over a 3-year period, considering the discontinuation of treatment as the disease progresses, was 6,547,693.07 rubles. Meanwhile, it was 5,962,742.52 rubles for “dabrafenib + trametinib” and 2,581,781.45 rubles for vemurafenib monotherapy. The incremental cost-effectiveness ratio of “encorafenib + binimetinib” vs. “dabrafenib + trametinib” was 3,846,818.71 rubles per 1 patient with a response to therapy. This was 85.14% lower than the same ratio estimated for “dabrafenib + trametinib” vs. vemurafenib monotherapy (25,890,022.64 rubles per 1 patient with a response to therapy).Conclusion. The “encorafenib + binimetinib” combination is a cost-effective treatment method for patients with unresectable or metastatic BRAF V600-mutated melanoma in the Russian Federation.

The Additional Disease Burden Accompanying a Delayed Diagnosis of Multiple Myeloma: Utilising a Large Real-World Evidence Base

Background Multiple myeloma (MM) is one of the most difficult cancers to diagnose, leading to one of the longest delays in diagnosis of all cancers. Delays in diagnosis of MM can reduce survival and lead to complications; however, the extent of disease burden patients experience has not been fully explored from the patient perspective. Our large-scale real-world surveys explore the symptom profiles, disease burden, and the downstream impact on the quality of life (QoL) of MM patients with a delayed versus timely diagnosis. Methods MM patients participated in two national surveys in the UK. Patients were asked to provide demographics, information on their symptoms at diagnosis, and whether they received a timely (<3 months from initial symptoms) or delayed diagnosis (3 months or more from their initial symptoms). Patients subsequently completed the PROMIS-29 v2.0 Health-Related QoL Questionnaire where they were asked to reflect on their current experiences; having completed at least 1 line of treatment. The questionnaire assessed seven health domains (i.e. physical function, fatigue, pain interference, depression, anxiety, social activity participation, and sleep disturbance) alongside a 10-item pain intensity scale. Results 586 MM patients completed Survey 1 and 617 patients completed Survey 2. Overall, patients reported a wide range of symptoms at diagnosis with ‘pain’ reported most frequently overall (74% of cases). Patients with a delayed diagnosis had significantly more symptoms ( M = 5, SD = 4), compared to those with a timely diagnosis ( M = 3, SD = 3), p < .001. MM patients with a delayed diagnosis also reported significantly more symptoms in 9 of the categories of symptoms (see Figure 1) compared to patients with a timely diagnosis, p < .001. Moreover, patients with a delayed diagnosis perceived more of their symptoms as having a ‘high impact’ on their daily lives at the time of diagnosis compared to those with a timely diagnosis, p < .001. The next analysis assessed the likely order that symptoms manifest at diagnosis depending on whether patients had a delayed or timely diagnosis. The most frequently experienced first symptom in patients with a timely or delayed diagnosis was ‘pain’. Decision tree analysis showed that patients with a timely diagnosis who experienced ‘pain’ as their first symptom, were 57% more likely to experience spinal fractures as their next symptom. Conversely, those with a delayed diagnosis were 83% more likely to experience mobility issues as their second symptom. The downstream impact of a delayed diagnosis revealed MM patients with a delayed diagnosis had significantly higher pain interference ( M = 57 vs 53), pain intensity ( M = 4 vs 3), sleep disturbance ( M = 53 vs 51), anxiety ( M = 56 vs 53), depression ( M = 53 vs 50) and fatigue ( M = 57 vs 55), compared to MM patients with a timely diagnosis. MM patients with a delayed diagnosis reported significantly lower physical functioning scores ( M = 43 vs 45) than patients with a timely diagnosis. Scores measuring the ability of patients to carry out social roles and activities were also significantly lower in patients with a timely than delayed diagnosis ( M = 42 vs 44). Conclusions Patients in these large-scale and real-world surveys experienced considerable disease burden at diagnosis when they had a delayed versus timely diagnosis, both in terms of frequency and range of symptoms. The results show the considerable impact of individual symptoms on the daily lives of patients at diagnosis, as well as the downstream impact delayed diagnosis has on the QoL of MM patients. The results offer a potential model for disease progression based on the staging of symptoms and a method for identifying MM patients in clinical practice.

Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices.

Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model-informed drug discovery and development (MID3). To foster a wider adoption and acceptance of these advanced algorithms, the Innovation and Quality (IQ) Consortium initiated the AI/ML working group in 2021 with the aim of promoting their acceptance among the broader scientific community as well as by regulatory agencies. By drawing insights from workshops organized by the working group and attended by key stakeholders across the biopharma industry, academia, and regulatory agencies, this white paper provides a perspective from the IQ Consortium. The range of applications covered in this white paper encompass the following thematic topics: (i) AI/ML-enabled Analytics for Pharmacometrics and Quantitative Systems Pharmacology (QSP) Workflows; (ii) Explainable Artificial Intelligence and its Applications in Disease Progression Modeling; (iii) Natural Language Processing (NLP) in Quantitative Pharmacology Modeling; and (iv) AI/ML Utilization in Drug Discovery. Additionally, the paper offers a set of best practices to ensure an effective and responsible use of AI, including considering the context of use, explainability and generalizability of models, and having human-in-the-loop. We believe that embracing the transformative power of AI in quantitative modeling while adopting a set of good practices can unlock new opportunities for innovation, increase efficiency, and ultimately bring benefits to patients.

Disease progression model anchored around clinical diagnosis in longitudinal cohorts: example of Alzheimer’s disease and related dementia

BackgroundAlzheimer’s disease and related dementia (ADRD) are characterized by multiple and progressive anatomo-clinical changes including accumulation of abnormal proteins in the brain, brain atrophy and severe cognitive impairment. Understanding the sequence and timing of these changes is of primary importance to gain insight into the disease natural history and ultimately allow earlier diagnosis. Yet, modeling changes over disease course from cohort data is challenging as the usual timescales (time since inclusion, chronological age) are inappropriate and time-to-clinical diagnosis is available on small subsamples of participants with short follow-up durations prior to diagnosis. One solution to circumvent this challenge is to define the disease time as a latent variable.MethodsWe developed a multivariate mixed model approach that realigns individual trajectories into the latent disease time to describe disease progression. In contrast with the existing literature, our methodology exploits the clinical diagnosis information as a partially observed and approximate reference to guide the estimation of the latent disease time. The model estimation was carried out in the Bayesian Framework using Stan. We applied the methodology to the MEMENTO study, a French multicentric clinic-based cohort of 2186 participants with 5-year intensive follow-up. Repeated measures of 12 ADRD markers stemmed from cerebrospinal fluid (CSF), brain imaging and cognitive tests were analyzed.ResultsThe estimated latent disease time spanned over twenty years before the clinical diagnosis. Considering the profile of a woman aged 70 with a high level of education and APOE4 carrier (the main genetic risk factor for ADRD), CSF markers of tau proteins accumulation preceded markers of brain atrophy by 5 years and cognitive decline by 10 years. However we observed that individual characteristics could substantially modify the sequence and timing of these changes, in particular for CSF level of Abeta _{42}.ConclusionBy leveraging the available clinical diagnosis timing information, our disease progression model does not only realign trajectories into the most homogeneous way. It accounts for the inherent residual inter-individual variability in dementia progression to describe the long-term anatomo-clinical degradations according to the years preceding clinical diagnosis, and to provide clinically meaningful information on the sequence of events.Trial registrationclinicaltrials.gov, NCT01926249. Registered on 16 August 2013.

Interaction of sex and onset site on the disease trajectory of amyotrophic lateral sclerosis.

Studies showed the impact of sex and onset site (spinal or bulbar) on disease onset and survival in ALS. However, they mainly result from cross-sectional or survival analysis, and the interaction of sex and onset site on the different proxies of disease trajectory has not been fully investigated. We selected all patients with repeated observations in the PRO-ACT database. We divided them into four groups depending on their sex and onset site. We estimated a multivariate disease progression model, named ALS Course Map, to investigate the combined temporal changes of the four sub-scores of the revised ALS functional rating scale (ALSFRSr), the forced vital capacity (FVC), and the body mass index (BMI). We then compared the progression rate, the estimated age at onset, and the relative progression of the outcomes across each group. We included 1438 patients from the PRO-ACT database. They were 51% men with spinal onset, 12% men with bulbar onset, 26% women with spinal onset, and 11% women with bulbar onset. We showed a significant influence of both sex and onset site on the ALSFRSr progression. The BMI decreased 8.9 months earlier (95% CI [3.9, 13.8]) in women than men, after correction for the onset site. Among patients with bulbar onset, FVC was impaired 2.6 months earlier (95% CI [0.6, 4.6]) in women. Using a multivariable disease modelling approach, we showed that sex and onset site are important drivers of the progression of motor function, BMI, and FVC decline.

From serological surveys to disease burden: a modelling pipeline for Chagas disease.

In 2012, the World Health Organization (WHO) set the elimination of Chagas disease intradomiciliary vectorial transmission as a goal by 2020. After a decade, some progress has been made, but the new 2021-2030 WHO roadmap has set even more ambitious targets. Innovative and robust modelling methods are required to monitor progress towards these goals. We present a modelling pipeline using local seroprevalence data to obtain national disease burden estimates by disease stage. Firstly, local seroprevalence information is used to estimate spatio-temporal trends in the Force-of-Infection (FoI). FoI estimates are then used to predict such trends across larger and fine-scale geographical areas. Finally, predicted FoI values are used to estimate disease burden based on a disease progression model. Using Colombia as a case study, we estimated that the number of infected people would reach 506 000 (95% credible interval (CrI) = 395 000-648 000) in 2020 with a 1.0% (95%CrI = 0.8-1.3%) prevalence in the general population and 2400 (95%CrI = 1900-3400) deaths (approx. 0.5% of those infected). The interplay between a decrease in infection exposure (FoI and relative proportion of acute cases) was overcompensated by a large increase in population size and gradual population ageing, leading to an increase in the absolute number of Chagas disease cases over time. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.

The temporal event-based model: Learning event timelines in progressive diseases.

Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here, we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer's disease (AD) and Huntington's disease (HD). In both diseases, the TEBM not only recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve a power of with less than half the cohort size compared with random selection. The application of the TEBM naturally extends to a wide range of progressive conditions.

Resolving the spatial architecture of myeloma and its microenvironment at the single-cell level

In multiple myeloma spatial differences in the subclonal architecture, molecular signatures and composition of the microenvironment remain poorly characterized. To address this shortcoming, we perform multi-region sequencing on paired random bone marrow and focal lesion samples from 17 newly diagnosed patients. Using single-cell RNA- and ATAC-seq we find a median of 6 tumor subclones per patient and unique subclones in focal lesions. Genetically identical subclones display different levels of spatial transcriptional plasticity, including nearly identical profiles and pronounced heterogeneity at different sites, which can include differential expression of immunotherapy targets, such as CD20 and CD38. Macrophages are significantly depleted in the microenvironment of focal lesions. We observe proportional changes in the T-cell repertoire but no site-specific expansion of T-cell clones in intramedullary lesions. In conclusion, our results demonstrate the relevance of considering spatial heterogeneity in multiple myeloma with potential implications for models of cell-cell interactions and disease progression.

Risk Assessment of Kidney Disease Progression and Efficacy of SGLT2 Inhibition in Patients With Type 2 Diabetes.

To develop a risk assessment tool to identify patients with type 2 diabetes (T2D) at higher risk for kidney disease progression and who might benefit more from sodium-glucose cotransporter 2 (SGLT2) inhibition. A total of 41,204 patients with T2D from four Thrombolysis In Myocardial Infarction (TIMI) clinical trials were divided into derivation (70%) and validation cohorts (30%). Candidate predictors of kidney disease progression (composite of sustained ≥40% decline in estimated glomerular filtration rate [eGFR], end-stage kidney disease, or kidney death) were selected with multivariable Cox regression. Efficacy of dapagliflozin was assessed by risk categories (low: <0.5%; intermediate: 0.5 to <2%; high: ≥2%) in Dapagliflozin Effect on Cardiovascular Events (DECLARE)-TIMI 58. There were 695 events over a median follow-up of 2.4 years. The final model comprised eight independent predictors of kidney disease progression: atherosclerotic cardiovascular disease, heart failure, systolic blood pressure, T2D duration, glycated hemoglobin, eGFR, urine albumin-to-creatinine ratio, and hemoglobin. The c-indices were 0.798 (95% CI, 0.774-0.821) and 0.798 (95% CI, 0.765-0.831) in the derivation and validation cohort, respectively. The calibration plot slope (deciles of predicted vs. observed risk) was 0.98 (95% CI, 0.93-1.04) in the validation cohort. Whereas relative risk reductions with dapagliflozin did not differ across risk categories, there was greater absolute risk reduction in patients with higher baseline risk, with a 3.5% absolute risk reduction in kidney disease progression at 4 years in the highest risk group (≥1%/year). Results were similar with the 2022 Chronic Kidney Disease Prognosis Consortium risk prediction model. Risk models for kidney disease progression can be applied in patients with T2D to stratify risk and identify those who experience a greater magnitude of benefit from SGLT2 inhibition.

BSBM-17 NOVEL VALIDATED PATIENT-DERIVED MODELS OF A RECTAL CANCER BRAIN METASTASIS

Abstract Colorectal adenocarcinomas (CRC) rarely metastasize to the brain, however, survival rates in patients with colorectal brain metastases (CBM) are short, with a median survival of only 4 months from diagnosis. Currently, no evidence-based treatment options against these rare brain metastases are available (PMC). Therefore, characterization of these tumors and development of targeted therapies are imperative. Optimal models for neoplasms heighten the notoriously low success rate of bench-to-bedside translation. Historically, brain cancer metastases models are established cell lines grown on plastic and transplanted as xenografts (Valiente; PMC7572582). Genomic drift, lack of genetic accuracy and heterogeneity, caused by serial passaging on plastic and in high-serum media, are frequent limitations of these models. Growth in defined media without serum and as 3-dimensional (3-D) spheroids or organoids is thought to improve the fidelity of cancer cell lines (Lee; PMID:16697959; Lo; PMC820864). Despite significant efforts of large collaborative projects (https://hcmi-searchable-catalog.nci.nih.gov/), 3-D rectal brain cancer metastasis models have not been reported. To address this unmet medical need, we developed 3-D (suspension, organoid) cultures of a rectal adenocarcinoma brain metastasis with moderate to focally poor differentiation. Next-generation sequencing, expression profiling, and immunohistochemistry were performed to validate our models. Our data confirmed that the PDO model contains tumor cells; pathogenic alterations and expression profiles in the models largely match that of the original tumor. Overall, we detected a significant phenotypic overlap with the original tumor sample. Spatial profiling and PDO-derived xenografts are in progress and will facilitate experimental therapeutics. Taken together, our models retained tumor heterogeneity and fidelity from the primary tumor, making this one of the first studies to establish 3-D patient-derived models for disease progression in rectal cancer brain metastases. Collectively, these models will be valuable translational tools for future exploration of targeted therapy, which remains absent in this disease process.

Multi-Task Deep Evidential Sequence Learning for Trustworthy Alzheimer’s Disease Progression Prediction

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease. Providing trustworthy AD progression predictions for at-risk individuals contributes to early identification of AD patients and holds significant value in discovering effective treatments and empowering the patient in taking proactive care. Recently, although numerous disease progression models based on machine learning have emerged, they often focus solely on enhancing predictive accuracy and ignore the measurement of result reliability. Consequently, this oversight adversely affects the recognition and acceptance of these models in clinical applications. To address these problems, we propose a multi-task evidential sequence learning model for the trustworthy prediction of disease progression. Specifically, we incorporate evidential deep learning into the multi-task learning framework based on recurrent neural networks. We simultaneously perform AD clinical diagnosis and cognitive score predictions while quantifying the uncertainty of each prediction without incurring additional computational costs by leveraging the Dirichlet and Normal-Inverse-Gamma distributions. Moreover, an adaptive weighting scheme is introduced to automatically balance between tasks for more effective training. Finally, experimental results on the TADPOLE dataset validate that our model not only has a comparable predictive performance to similar models but also offers reliable quantification of prediction uncertainties, providing a crucial supplementary factor for risk-sensitive AD progression prediction applications.

Cost-Effectiveness of Single-Inhaler Triple Therapy (FF/UMEC/VI) versus Tiotropium Monotherapy in Patients with Symptomatic Moderate-to-Very Severe COPD in the UK.

For patients with chronic obstructive pulmonary disease (COPD) who remain symptomatic despite maintenance treatment, clinical management guidelines recommend a stepwise escalation from monotherapy to dual therapy, and from dual therapy to triple therapy. However, in clinical practice, patients are often escalated directly from monotherapy to triple therapy based on disease severity. This study evaluated the cost-effectiveness of once-daily, single-inhaler fluticasone furoate, umeclidinium, and vilanterol (FF/UMEC/VI) triple therapy compared with long-acting muscarinic antagonist monotherapy with once-daily tiotropium (TIO) in patients with symptomatic moderate-to-very severe COPD, from a UK National Health Service perspective. The validated GALAXY-COPD disease progression model was populated with patient baseline characteristics and treatment effect data from the 12-week GSK Study 207626 comparing FF/UMEC/VI with TIO in patients with moderate-to-very severe COPD. UK unit costs and drug costs (British Pound, 2021) were applied to healthcare resource utilization and treatments. The base case analysis was conducted over a lifetime horizon, and costs and health outcomes (except for life years [LYs]) were discounted at 3.5% per year. Model outputs included exacerbation rates, healthcare costs, LYs, quality-adjusted LYs (QALYs), and incremental cost-effectiveness ratios. Overall, treatment with FF/UMEC/VI resulted in increased clinical benefit (reduction in total exacerbations and increased overall survival and QALYs), coupled with cost savings (derived from lower maintenance and exacerbation healthcare costs) compared with TIO monotherapy. In the base case analysis, FF/UMEC/VI provided an additional 0.393 LYs (95% range: 0.176, 0.655) and 0.443 QALYs (0.246, 0.648), at a cost saving of £880 (£54, £1608) versus TIO. FF/UMEC/VI remained the cost-effective (dominant) treatment option across sensitivity and scenario analyses. FF/UMEC/VI offers greater clinical benefits and is a cost-effective treatment option compared with TIO for the treatment of adult patients with COPD with persistent symptoms and/or who are at risk of exacerbation in the UK.

Clinical Pharmacology Applications of Real-World Data and Real-World Evidence in Drug Development and Approval-An Industry Perspective.

Since the 21st Century Cures Act was signed into law in 2016, real-world data (RWD) and real-world evidence (RWE) have attracted great interest from the healthcare ecosystem globally. The potential and capability of RWD/RWE to inform regulatory decisions and clinical drug development have been extensively reviewed and discussed in the literature. However, a comprehensive review of current applications of RWD/RWE in clinical pharmacology, particularly from an industry perspective, is needed to inspire new insights and identify potential future opportunities for clinical pharmacologists to utilize RWD/RWE to address key drug development questions. In this paper, we review the RWD/RWE applications relevant to clinical pharmacology based on recent publications from member companies in the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) RWD Working Group, and discuss the future direction of RWE utilization from a clinical pharmacology perspective. A comprehensive review of RWD/RWE use cases is provided and discussed in the following categories of application: drug-drug interaction assessments, dose recommendation for patients with organ impairment, pediatric plan development and study design, model-informed drug development (e.g., disease progression modeling), prognostic and predictive biomarkers/factors identification, regulatory decisions support (e.g., label expansion), and synthetic/external control generation for rare diseases. Additionally, we describe and discuss common sources of RWD to help guide appropriate data selection to address questions pertaining to clinical pharmacology in drug development and regulatory decision making.

Cost-effectiveness of integrated treatment for hepatitis C virus (HCV) among people who inject drugs in Norway: An economic evaluation of the INTRO-HCV trial.

The INTRO-HCV randomized controlled trial conducted in Norway over 2017-2019 found that integrated treatment, compared with standard-of-care hospital treatment, for hepatitis C virus (HCV) with direct-acting antivirals (DAAs) improved treatment outcomes among people who inject drugs (PWID). We evaluated cost-effectiveness of the INTRO-HCV intervention. A Markov health state transition model of HCV disease progression and treatment with cost-effectiveness analysis from the health-provider perspective. Primary cost, utility, and health outcome data were derived from the trial. Costs and health benefits (quality-adjusted life-years, QALYs) were tracked over 50 years. Probabilistic and univariate sensitivity analyses investigated DAA price reductions and variations in HCV treatment and disease care cost assumptions, using costs from different countries (Norway, United Kingdom, United States, France, Australia). PWID attending community-based drug treatment centers for people with opioid dependence in Norway. Incremental cost-effectiveness ratio (ICER) in terms of cost per QALY gained, compared against a conventional (€70 000/QALY) willingness-to-pay threshold for Norway and lower (€20 000/QALY) threshold common among high-income countries. Integrated treatment resulted in an ICER of €13 300/QALY gained, with 99% and 71% probability of being cost-effective against conventional and lower willingness-to-pay thresholds, respectively. A 30% lower DAA price reduced the ICER to €6 900/QALY gained, with 91% probability of being cost-effective at the lower willingness-to-pay threshold. A 60% and 90% lower DAA price had 36% and >99% probability of being cost-saving, respectively. Sensitivity analyses suggest integrated treatment was cost-effective at the lower willingness-to-pay threshold (>60% probability) across different assumptions on HCV treatment and disease care costs with 30% DAA price reduction, and became cost-saving with 60%-90% price reductions. Integrated hepatitis C virus treatment for people who inject drugs in community settings is likely cost-effective compared with standard-of-care referral pathways in Norway and may be cost-saving in settings with particular characteristics.

Disease progression modelling of Alzheimer’s disease using probabilistic principal components analysis

The recent biological redefinition of Alzheimer’s Disease (AD) has spurred the development of statistical models that relate changes in biomarkers with neurodegeneration and worsening condition linked to AD. The ability to measure such changes may facilitate earlier diagnoses for affected individuals and help in monitoring the evolution of their condition. Amongst such statistical tools, disease progression models (DPMs) are quantitative, data-driven methods that specifically attempt to describe the temporal dynamics of biomarkers relevant to AD. Due to the heterogeneous nature of this disease, with patients of similar age experiencing different AD-related changes, a challenge facing longitudinal mixed-effects-based DPMs is the estimation of patient-realigning time-shifts. These time-shifts are indispensable for meaningful biomarker modelling, but may impact fitting time or vary with missing data in jointly estimated models. In this work, we estimate an individual’s progression through Alzheimer’s disease by combining multiple biomarkers into a single value using a probabilistic formulation of principal components analysis. Our results show that this variable, which summarises AD through observable biomarkers, is remarkably similar to jointly estimated time-shifts when we compute our scores for the baseline visit, on cross-sectional data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Reproducing the expected properties of clinical datasets, we confirm that estimated scores are robust to missing data or unavailable biomarkers. In addition to cross-sectional insights, we can model the latent variable as an individual progression score by repeating estimations at follow-up examinations and refining long-term estimates as more data is gathered, which would be ideal in a clinical setting. Finally, we verify that our score can be used as a pseudo-temporal scale instead of age to ignore some patient heterogeneity in cohort data and highlight the general trend in expected biomarker evolution in affected individuals.

A KNN Algorithm Based Predictive Model for Heart Disease Progression

Background: Disease diagnosis was a vital responsibility in healthcare. Machine learning classification methods would considerably improve the healthcare industry by providing a quick diagnosis of disease. Thus, time could be saved for doctors. Nearly 17.9 million people expire due to heart disease every year. Objectives: World Health Organization predicted that rate of death might increase by 24.5 million in 2030. Since heart illness was the major cause of death in comparison with other diseases today, it was the most challenging disease to diagnose. Methods: One of the reasons for death due to heart disease was due to the fact that risks were not identified in the earlier stage. Earlier diagnosis of disease was very much important. Machine Learning algorithms were used for predicting the prognosis of disease. Results: Here K-NN algorithm was used to predict the presence of heart disease in an individual. Thus, patients were classified as either positive or negative for heart disease and this model enhanced medical care and reduced the cost. This gave us significant knowledge that helps us to predict the patients with heart disease. Conclusion: The Python sci-kit library was used to implement this in Anaconda Navigator's Spyder Integrated Development Environment. Experiments revealed that technique worked well and was more accurate than before.

Predictori ai evoluţiei clinice a schizofreniei

Introduction. Schizophrenia is a serious disorder that influences all aspects of the patients’ life. The most important goals in schizophrenia are remission, recovery, and improving psychosocial functioning and quality of life. Unfortunately, there are important barriers to achieving optimal long-term outcomes in this disease due to patient-related factors and treatment-related factors. Lack of insight is considered the main reason for partial/non-adherence and, therefore, for the long-term outcome. Aim. The aim of this study was to identify the predictive factors involved in long-term schizophrenia, and especially the role of the awareness of illness. Materials and method. Eighty patients (44 males and 36 females) recruited from the First and Second Psychiatric Clinic from Cluj-Napoca, diagnosed according to ICD-10 criteria with schizophrenia, participated in this study. A semi-structured interview collected the demographical data. Psychotic symptoms were evaluated using Positive and Negative Syndrome Scale (PANSS), severity of the disease using Clinical Global Impression (CGI) and insight using the Scale for the Assessment of Unawareness of Mental Disorder (SUMD), the Schedule for Assessment of Insight-Expanded version (SAI-E) and Beck Cognitive Insight Scale (BCIS). Results. Our results showed that, among potential predictors of psychotic symptoms change measured in this research, those significantly correlated with changes are the level of insight measured with SUMD scale (r=-0.41; p&lt;0.01), presence of family history (r=0.24; p&lt;0.05), and belonging to urban areas (r=0.23; p&lt;0.05). The level of insight, as one predictor, with PANSS initially controlled, explained 16% of variance of improving psychotic symptoms during hospitalization. Also, patients from urban areas seem to have a slight tendency towards higher magnitudes of the changes (low correlation, but statistically significant). Again, the square of the correlation obtained (coefficient of determination) indicates that urban membership explaines 5% of variance when symptoms improve. Also, it seems that the family history is positively associated with the magnitude of symptoms’ change, but the level of this relationship is also reduced. Conclusions. The awareness of illness is one of the predictive factors for long-term schizophrenia, and the best predictive model of disease progression is composed of variables SUMD total and PANSS total on admission.